Container Scheduling Strategy Based on Image Layer Reuse and Sequential Arrangement in Mobile Edge Computing

In Mobile Edge Computing (MEC) scenarios, computational tasks are popularly deployed using containerization to isolate the runtime environment. To complete the execution of the task, the edge server first pulls the image, then instantiates and runs the container. Since it takes a lot of time for the edge server to download the image from the cloud, image reuse reduces the pulling latency significantly. However, the limited storage capacity of edge servers hinders image reuse. Recent works have enhanced reuse efficiency by leveraging the hierarchical structure of images and caching high-value layers. However, their efficiency remains limited due to the lack of multi-container collaboration. This paper proposes a novel container scheduling strategy based on image layer reuse and sequence arrangement (ILR-SA) for MEC scenarios, which achieves efficient scheduling by collaborating multiple containers. First, containers are greedily deployed into the edge cluster. Then, the execution sequence of containers is modeled as an optimal Hamiltonian path problem, efficiently solved by our proposed decomposition algorithm. Finally, an efficient image layer update strategy is used to achieve layer reuse. We conduct rigorous experiments to demonstrate that our proposed container scheduling strategy reduces the computational task completion time by up to 91.3% compared to existing approaches.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Mobile Computing
Published
2025-04-02
DOI
https://doi.org/10.1109/tmc.2025.3557160
Citations
3
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
4.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Container Scheduling Strategy Based on Image Layer Reuse and Sequential Arrangement in Mobile Edge Computing

Weiwei Lin, Albert Y. Zomaya, Wangbo Shen, Keqin Li et al.
3 citations
IEEE Transactions on Mobile Computing
IoT and Edge/Fog Computing
4.46
article

Container Scheduling Strategy Based on Image Layer Reuse and Sequential Arrangement in Mobile Edge Computing

Weiwei Lin, Albert Y. Zomaya, Wangbo Shen, Keqin Li, Haijie Wu, Haotong Zhang, Fang Shi
article en
3 citations

Abstract

In Mobile Edge Computing (MEC) scenarios, computational tasks are popularly deployed using containerization to isolate the runtime environment. To complete the execution of the task, the edge server first pulls the image, then instantiates and runs the container. Since it takes a lot of time for the edge server to download the image from the cloud, image reuse reduces the pulling latency significantly. However, the limited storage capacity of edge servers hinders image reuse. Recent works have enhanced reuse efficiency by leveraging the hierarchical structure of images and caching high-value layers. However, their efficiency remains limited due to the lack of multi-container collaboration. This paper proposes a novel container scheduling strategy based on image layer reuse and sequence arrangement (ILR-SA) for MEC scenarios, which achieves efficient scheduling by collaborating multiple containers. First, containers are greedily deployed into the edge cluster. Then, the execution sequence of containers is modeled as an optimal Hamiltonian path problem, efficiently solved by our proposed decomposition algorithm. Finally, an efficient image layer update strategy is used to achieve layer reuse. We conduct rigorous experiments to demonstrate that our proposed container scheduling strategy reduces the computational task completion time by up to 91.3% compared to existing approaches.

IEEE Transactions on Mobile ComputingVol. 24(9)
South China Agricultural University (CN), The University of Sydney (AU), SUNY New Paltz (US), South China University of Technology (CN)
Openalex Percentile: Top 6%
IoT and Edge/Fog Computing
4.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.